Overview

Dataset statistics

Number of variables68
Number of observations15207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 MiB
Average record size in memory537.0 B

Variable types

Numeric5
Boolean1
Categorical62

Alerts

pct_disc is highly overall correlated with pct_retail_discHigh correlation
pct_retail_disc is highly overall correlated with pct_discHigh correlation
marital_status_A is highly overall correlated with hhsize_1High correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_1 and 1 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with hhsize_2High correlation
kid_category_1 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_4High correlation
kid_category_3+ is highly overall correlated with hhsize_5+High correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
age_35-44 is highly overall correlated with age_45-54High correlation
age_45-54 is highly overall correlated with age_35-44High correlation
hhsize_1 is highly overall correlated with marital_status_AHigh correlation
hhsize_2 is highly overall correlated with hhcomp_2 Adults No KidsHigh correlation
hhsize_3 is highly overall correlated with kid_category_1 and 1 other fieldsHigh correlation
hhsize_4 is highly overall correlated with kid_category_2High correlation
hhsize_5+ is highly overall correlated with kid_category_3+High correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_18.0 is highly overall correlated with description_TypeAHigh correlation
description_TypeA is highly overall correlated with campaign_13.0 and 1 other fieldsHigh correlation
display_1 is highly imbalanced (63.7%)Imbalance
display_2 is highly imbalanced (88.6%)Imbalance
display_3 is highly imbalanced (87.1%)Imbalance
display_4 is highly imbalanced (94.8%)Imbalance
display_5 is highly imbalanced (84.1%)Imbalance
display_6 is highly imbalanced (94.9%)Imbalance
display_7 is highly imbalanced (78.4%)Imbalance
display_9 is highly imbalanced (87.6%)Imbalance
display_A is highly imbalanced (94.5%)Imbalance
mailer_C is highly imbalanced (99.8%)Imbalance
mailer_D is highly imbalanced (93.4%)Imbalance
mailer_F is highly imbalanced (99.8%)Imbalance
mailer_H is highly imbalanced (84.9%)Imbalance
mailer_J is highly imbalanced (96.9%)Imbalance
mailer_L is highly imbalanced (99.8%)Imbalance
homeowner_Probable Owner is highly imbalanced (87.5%)Imbalance
homeowner_Probable Renter is highly imbalanced (91.0%)Imbalance
homeowner_Renter is highly imbalanced (64.3%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (64.6%)Imbalance
hhcomp_Single Male is highly imbalanced (55.1%)Imbalance
kid_category_2 is highly imbalanced (51.6%)Imbalance
age_19-24 is highly imbalanced (72.2%)Imbalance
age_55-64 is highly imbalanced (69.7%)Imbalance
age_65+ is highly imbalanced (64.2%)Imbalance
income_100-124K is highly imbalanced (75.3%)Imbalance
income_125-149K is highly imbalanced (67.2%)Imbalance
income_15-24K is highly imbalanced (60.7%)Imbalance
income_150-174K is highly imbalanced (75.4%)Imbalance
income_175-199K is highly imbalanced (87.5%)Imbalance
income_200-249K is highly imbalanced (96.4%)Imbalance
income_25-34K is highly imbalanced (53.1%)Imbalance
income_250K+ is highly imbalanced (83.5%)Imbalance
income_Under 15K is highly imbalanced (61.4%)Imbalance
hhsize_4 is highly imbalanced (56.3%)Imbalance
hhsize_5+ is highly imbalanced (51.7%)Imbalance
campaign_8.0 is highly imbalanced (98.1%)Imbalance
campaign_13.0 is highly imbalanced (98.0%)Imbalance
campaign_18.0 is highly imbalanced (97.1%)Imbalance
campaign_25.0 is highly imbalanced (99.9%)Imbalance
campaign_26.0 is highly imbalanced (99.5%)Imbalance
campaign_30.0 is highly imbalanced (99.9%)Imbalance
description_TypeA is highly imbalanced (93.9%)Imbalance
description_TypeB is highly imbalanced (99.9%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 8895 (58.5%) zerosZeros
pct_retail_disc has 8962 (58.9%) zerosZeros
pct_coupon_disc has 15099 (99.3%) zerosZeros

Reproduction

Analysis started2023-05-23 14:56:17.870610
Analysis finished2023-05-23 14:56:52.825596
Duration34.95 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct15207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13935.074
Minimum0
Maximum27405
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:56:52.998434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1400.3
Q17024.5
median13985
Q321252.5
95-th percentile26197.7
Maximum27405
Range27405
Interquartile range (IQR)14228

Descriptive statistics

Standard deviation8103.2073
Coefficient of variation (CV)0.58149724
Kurtosis-1.2672645
Mean13935.074
Median Absolute Deviation (MAD)7205
Skewness-0.022442935
Sum2.1191068 × 108
Variance65661969
MonotonicityStrictly increasing
2023-05-23T16:56:53.198449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
18832 1
 
< 0.1%
18820 1
 
< 0.1%
18821 1
 
< 0.1%
18822 1
 
< 0.1%
18823 1
 
< 0.1%
18824 1
 
< 0.1%
18825 1
 
< 0.1%
18826 1
 
< 0.1%
18827 1
 
< 0.1%
Other values (15197) 15197
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
27405 1
< 0.1%
27404 1
< 0.1%
27403 1
< 0.1%
27402 1
< 0.1%
27401 1
< 0.1%
27400 1
< 0.1%
27399 1
< 0.1%
27398 1
< 0.1%
27397 1
< 0.1%
27396 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
True
10846 
False
4361 
ValueCountFrequency (%)
True 10846
71.3%
False 4361
28.7%
2023-05-23T16:56:53.411348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct287
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1785747
Minimum0.1
Maximum57.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:56:53.569924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.39
median1.79
Q32.79
95-th percentile4.79
Maximum57.57
Range57.47
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.6888433
Coefficient of variation (CV)0.77520559
Kurtosis96.385061
Mean2.1785747
Median Absolute Deviation (MAD)0.71
Skewness5.6167503
Sum33129.586
Variance2.8521918
MonotonicityNot monotonic
2023-05-23T16:56:53.778385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.49 2332
15.3%
1.99 1276
 
8.4%
0.99 1261
 
8.3%
2.89 859
 
5.6%
2.99 741
 
4.9%
1.79 675
 
4.4%
3.19 647
 
4.3%
1.5 613
 
4.0%
2.5 537
 
3.5%
1.39 513
 
3.4%
Other values (277) 5753
37.8%
ValueCountFrequency (%)
0.1 5
 
< 0.1%
0.125 1
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.2 11
 
0.1%
0.25 143
0.9%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
0.3 1
 
< 0.1%
0.32 1
 
< 0.1%
ValueCountFrequency (%)
57.57 1
 
< 0.1%
27.99 1
 
< 0.1%
21.55 1
 
< 0.1%
19.99 3
 
< 0.1%
19.49 5
< 0.1%
15.99 3
 
< 0.1%
14.99 11
0.1%
12.49 4
 
< 0.1%
12.29 4
 
< 0.1%
11.99 2
 
< 0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct433
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1303918
Minimum0
Maximum0.93079585
Zeros8895
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:56:54.008362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.32663317
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.32663317

Descriptive statistics

Standard deviation0.17937756
Coefficient of variation (CV)1.3756813
Kurtosis0.47398231
Mean0.1303918
Median Absolute Deviation (MAD)0
Skewness1.1477682
Sum1982.8681
Variance0.03217631
MonotonicityNot monotonic
2023-05-23T16:56:54.200942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8895
58.5%
0.3288590604 1889
 
12.4%
0.1349480969 338
 
2.2%
0.2805755396 269
 
1.8%
0.3333333333 164
 
1.1%
0.5 158
 
1.0%
0.4949494949 148
 
1.0%
0.1039426523 143
 
0.9%
0.4974874372 120
 
0.8%
0.2163009404 107
 
0.7%
Other values (423) 2976
 
19.6%
ValueCountFrequency (%)
0 8895
58.5%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8316498316 1
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.8207885305 1
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct382
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12800657
Minimum-0
Maximum0.93079585
Zeros8962
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:56:54.433798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q30.28434504
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.28434504

Descriptive statistics

Standard deviation0.17757929
Coefficient of variation (CV)1.3872669
Kurtosis0.52178453
Mean0.12800657
Median Absolute Deviation (MAD)0
Skewness1.1643207
Sum1946.596
Variance0.031534403
MonotonicityNot monotonic
2023-05-23T16:56:54.642841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 8962
58.9%
0.3288590604 1853
 
12.2%
0.1349480969 339
 
2.2%
0.2805755396 274
 
1.8%
0.3333333333 164
 
1.1%
0.5 154
 
1.0%
0.4949494949 151
 
1.0%
0.1039426523 147
 
1.0%
0.4974874372 124
 
0.8%
0.2163009404 107
 
0.7%
Other values (372) 2932
 
19.3%
ValueCountFrequency (%)
-0 8962
58.9%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%
0.7759197324 1
< 0.1%
0.76 1
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023852234
Minimum-0
Maximum0.71684588
Zeros15099
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:56:54.850156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q3-0
95-th percentile-0
Maximum0.71684588
Range0.71684588
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.030190347
Coefficient of variation (CV)12.657241
Kurtosis212.38719
Mean0.0023852234
Median Absolute Deviation (MAD)0
Skewness14.021251
Sum36.272092
Variance0.00091145703
MonotonicityNot monotonic
2023-05-23T16:56:55.058525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0 15099
99.3%
0.3344481605 13
 
0.1%
0.3134796238 10
 
0.1%
0.5025125628 9
 
0.1%
0.2602230483 9
 
0.1%
0.4 7
 
< 0.1%
0.4366812227 7
 
< 0.1%
0.2 6
 
< 0.1%
0.5 4
 
< 0.1%
0.2132196162 3
 
< 0.1%
Other values (33) 40
 
0.3%
ValueCountFrequency (%)
-0 15099
99.3%
0.1002004008 1
 
< 0.1%
0.119474313 1
 
< 0.1%
0.1251564456 1
 
< 0.1%
0.1333333333 2
 
< 0.1%
0.1432664756 1
 
< 0.1%
0.1474926254 1
 
< 0.1%
0.1519756839 1
 
< 0.1%
0.159453303 1
 
< 0.1%
0.1618122977 1
 
< 0.1%
ValueCountFrequency (%)
0.7168458781 1
 
< 0.1%
0.6659707724 1
 
< 0.1%
0.5917159763 1
 
< 0.1%
0.5586592179 2
 
< 0.1%
0.5291005291 2
 
< 0.1%
0.5050505051 1
 
< 0.1%
0.5025125628 9
0.1%
0.5 4
< 0.1%
0.4566210046 1
 
< 0.1%
0.4366812227 7
< 0.1%

display_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14154 
1
 
1053

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Length

2023-05-23T16:56:55.268695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:55.459444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14975 
1
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Length

2023-05-23T16:56:55.608802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:55.798545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14936 
1
 
271

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Length

2023-05-23T16:56:55.911901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:56.033698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15117 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Length

2023-05-23T16:56:56.133313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:56.238571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14855 
1
 
352

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Length

2023-05-23T16:56:56.329515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:56.438677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15120 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Length

2023-05-23T16:56:56.528482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:56.628692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14685 
1
 
522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Length

2023-05-23T16:56:56.720875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:56.978576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14948 
1
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Length

2023-05-23T16:56:57.068455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:57.168657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

display_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15112 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Length

2023-05-23T16:56:57.248916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:57.348815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13509 
1
1698 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Length

2023-05-23T16:56:57.438699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:57.541679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:56:57.628615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:57.728530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_D
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15089 
1
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Length

2023-05-23T16:56:57.818637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:57.918512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

mailer_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:56:58.010932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:58.108437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14878 
1
 
329

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Length

2023-05-23T16:56:58.208449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:58.314961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

mailer_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15158 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Length

2023-05-23T16:56:58.400898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:58.508711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

mailer_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:56:58.595804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:58.698675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
8551 
1
6656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Length

2023-05-23T16:56:58.783524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:58.893507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring characters

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13106 
1
2101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Length

2023-05-23T16:56:58.978441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:59.080495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9675 
0
5532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Length

2023-05-23T16:56:59.178471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:59.283415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring characters

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14946 
1
 
261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Length

2023-05-23T16:56:59.386371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:59.498769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15033 
1
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Length

2023-05-23T16:56:59.588691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:59.692158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14177 
1
 
1030

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Length

2023-05-23T16:56:59.778872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:56:59.908486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14191 
1
 
1016

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Length

2023-05-23T16:56:59.998538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:00.118477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10766 
1
4441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Length

2023-05-23T16:57:00.208440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:00.352052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring characters

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10825 
1
4382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Length

2023-05-23T16:57:00.480466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:00.621657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring characters

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12825 
1
2382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Length

2023-05-23T16:57:00.731824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:00.848739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13781 
1
1426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Length

2023-05-23T16:57:00.943571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:01.060873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12976 
1
2231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Length

2023-05-23T16:57:01.148523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:01.268469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13612 
1
1595 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Length

2023-05-23T16:57:01.398426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:01.818427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

kid_category_3+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13474 
1
1733 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Length

2023-05-23T16:57:01.913868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.012755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9648 
0
5559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Length

2023-05-23T16:57:02.108630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.208422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring characters

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

age_19-24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14478 
1
 
729

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Length

2023-05-23T16:57:02.305868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.405481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

age_25-34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12878 
1
2329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Length

2023-05-23T16:57:02.491786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.601620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

age_35-44
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
11104 
1
4103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Length

2023-05-23T16:57:02.688572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.788811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring characters

ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

age_45-54
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
9016 
1
6191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Length

2023-05-23T16:57:02.884154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:02.984035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring characters

ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

age_55-64
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14385 
1
 
822

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Length

2023-05-23T16:57:03.073492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:03.178506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

age_65+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14174 
1
 
1033

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Length

2023-05-23T16:57:03.262150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:03.360885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

income_100-124K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14582 
1
 
625

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Length

2023-05-23T16:57:03.448447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:03.628424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

income_125-149K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14294 
1
 
913

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Length

2023-05-23T16:57:03.838417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:03.958438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

income_15-24K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14031 
1
 
1176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Length

2023-05-23T16:57:04.048840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:04.158674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

income_150-174K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14586 
1
 
621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Length

2023-05-23T16:57:04.251542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:04.368449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

income_175-199K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14947 
1
 
260

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Length

2023-05-23T16:57:04.468475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:04.583379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

income_200-249K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15149 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Length

2023-05-23T16:57:04.681986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:04.788423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

income_25-34K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13687 
1
1520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Length

2023-05-23T16:57:04.878485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:04.984875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

income_250K+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14838 
1
 
369

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Length

2023-05-23T16:57:05.078534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:05.180905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

income_35-49K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12300 
1
2907 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Length

2023-05-23T16:57:05.290830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:05.438810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

income_50-74K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
11470 
1
3737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Length

2023-05-23T16:57:05.588638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:05.728822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

income_75-99K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13332 
1
1875 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Length

2023-05-23T16:57:05.868402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:06.009891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

income_Under 15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14061 
1
 
1146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Length

2023-05-23T16:57:06.148408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:06.290852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

hhsize_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10604 
1
4603 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Length

2023-05-23T16:57:06.412450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:06.578712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring characters

ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

hhsize_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
9925 
1
5282 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Length

2023-05-23T16:57:06.708505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:06.850897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring characters

ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

hhsize_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12846 
1
2361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Length

2023-05-23T16:57:06.988829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:07.158492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

hhsize_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13834 
1
 
1373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Length

2023-05-23T16:57:07.308371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:07.488412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

hhsize_5+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13619 
1
1588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Length

2023-05-23T16:57:07.630059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:07.788798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

campaign_8.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15180 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Length

2023-05-23T16:57:07.922879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:08.073494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15178 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Length

2023-05-23T16:57:08.186989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:08.338808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

campaign_18.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15162 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Length

2023-05-23T16:57:08.790606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:08.928844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

campaign_25.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:09.048840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:09.198613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

campaign_26.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15201 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Length

2023-05-23T16:57:09.328383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:09.470489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:09.604292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:09.748878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

description_TypeA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15099 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Length

2023-05-23T16:57:09.883045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:10.018806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:10.150568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:10.288857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Interactions

2023-05-23T16:56:48.838700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:46.368758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.064293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.698598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.268873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.958525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:46.601855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.218489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.813540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.388541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:49.082655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:46.711615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.344475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.918490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.498750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:49.198700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:46.820438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.458505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.028896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.608850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:49.350367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:46.930531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:47.573126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.138870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:56:48.718813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-23T16:57:10.693698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discfirst_purchasedisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
Unnamed: 01.000-0.0170.0490.0490.0040.0490.0380.0310.0230.0280.0340.0190.0260.0120.0440.0360.0280.0000.0000.0160.0270.0000.1140.1480.1670.2030.1400.1750.1440.0760.1060.0810.2110.1310.1470.1750.1000.1350.1080.1040.1380.1080.1660.1320.2040.1810.1180.1490.1690.1310.1520.1220.1790.1490.1380.1190.1280.1520.2230.1320.0170.0000.0240.0060.0000.0010.0200.006
shelf_price-0.0171.000-0.001-0.0080.0640.0550.0000.0090.0050.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0260.0000.0280.0080.0000.0000.0000.0100.0260.0200.0140.0000.0000.0180.0050.0000.0000.0000.0130.0360.0000.0000.0000.0240.0390.0000.0200.0120.0000.0000.0000.0280.0000.0040.0200.0000.0000.0190.0000.1850.0000.0000.0000.0000.0940.000
pct_disc0.049-0.0011.0000.9880.1240.1500.2180.1070.0870.0680.0780.0300.0910.0670.0110.4090.0000.1430.0000.1830.0790.0450.0480.0280.0700.0230.0000.0550.0380.0280.0540.0160.0460.0000.0260.0110.0410.0380.0440.0380.0190.0170.0390.0450.0000.0670.0500.0440.0180.0430.0430.0440.0240.0360.0480.0390.0570.0220.0500.0140.0630.0500.0380.0170.0380.0000.0320.017
pct_retail_disc0.049-0.0080.9881.000-0.0150.1520.2210.1080.0890.0770.0800.0310.0910.0680.0110.4130.0000.1430.0000.1870.0800.0460.0520.0280.0750.0210.0000.0560.0420.0310.0530.0170.0510.0120.0300.0130.0420.0380.0450.0360.0230.0190.0400.0440.0000.0680.0520.0430.0180.0410.0420.0470.0260.0410.0490.0400.0540.0270.0490.0170.0640.0190.0320.0170.0460.0000.0360.017
pct_coupon_disc0.0040.0640.124-0.0151.0000.0260.0000.0000.0000.0240.0000.0000.0210.0000.0000.0000.0000.0000.0000.0330.0000.0000.0140.0300.0240.0000.0000.0140.0000.0150.0000.0000.0240.0110.0000.0200.0000.0000.0180.0220.0070.0000.0170.0120.0350.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0090.0000.0250.0000.2050.2150.0000.0700.0000.1860.000
first_purchase0.0490.0550.1500.1520.0261.0000.0100.0120.0130.0120.0170.0000.0000.0120.0090.0320.0000.0000.0000.0000.0180.0000.0220.0120.0040.0180.0150.0210.0070.0320.0060.0340.0160.0320.0000.0080.0360.0000.0300.0230.0000.0050.0630.0000.0270.0100.0410.0260.0250.0350.0000.0110.0120.0160.0000.0420.0060.0240.0120.0310.0140.0150.0330.0000.0040.0000.0380.000
display_10.0380.0000.2180.2210.0000.0101.0000.0320.0350.0180.0400.0170.0500.0340.0180.1520.0000.0710.0000.0810.0220.0000.0000.0000.0090.0080.0020.0080.0190.0000.0080.0000.0010.0000.0100.0000.0080.0000.0000.0000.0000.0000.0000.0270.0050.0280.0110.0240.0000.0280.0220.0000.0150.0350.0240.0000.0000.0000.0080.0000.0000.0040.0090.0000.0000.0000.0090.000
display_20.0310.0090.1070.1080.0000.0120.0321.0000.0120.0000.0150.0000.0200.0120.0000.0700.0000.0140.0000.0380.0000.0000.0150.0140.0040.0000.0000.0000.0290.0000.0110.0000.0000.0080.0200.0150.0070.0000.0210.0120.0000.0050.0000.0140.0000.0000.0170.0000.0000.0000.0000.0180.0000.0020.0090.0000.0000.0000.0000.0110.0110.0000.0000.0000.0000.0000.0000.000
display_30.0230.0050.0870.0890.0000.0130.0350.0121.0000.0000.0170.0000.0230.0140.0000.0570.0000.0000.0000.0000.0000.0000.0130.0000.0010.0000.0000.0130.0000.0090.0000.0070.0110.0140.0000.0000.0090.0000.0000.0000.0090.0150.0000.0000.0210.0000.0000.0010.0000.0000.0000.0120.0000.0000.0040.0000.0090.0130.0030.0000.0000.0000.0000.0000.0060.0000.0000.000
display_40.0280.0000.0680.0770.0240.0120.0180.0000.0001.0000.0040.0000.0090.0000.0000.0270.0000.0000.0000.0000.0000.0000.0190.0000.0130.0000.0000.0080.0130.0040.0000.0000.0030.0000.0000.0060.0130.0000.0000.0000.0020.0000.0090.0050.0000.0160.0000.0000.0000.0230.0000.0230.0000.0050.0070.0130.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.000
display_50.0340.0000.0780.0800.0000.0170.0400.0150.0170.0041.0000.0030.0270.0170.0050.0330.0000.0000.0000.0000.0000.0000.0200.0030.0370.0160.0060.0130.0380.0000.0130.0000.0020.0080.0180.0120.0110.0290.0000.0130.0050.0100.0080.0160.0000.0090.0070.0000.0000.0050.0080.0000.0000.0000.0160.0000.0120.0000.0210.0000.0180.0000.0000.0000.0000.0240.0000.000
display_60.0190.0000.0300.0310.0000.0000.0170.0000.0000.0000.0031.0000.0090.0000.0000.0260.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0020.0080.0000.0000.0000.0000.0160.0000.0000.0050.0000.0040.0000.0140.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0020.0060.0150.0000.0000.0170.0000.0000.0270.0000.0000.0000.0000.0000.0040.000
display_70.0260.0000.0910.0910.0210.0000.0500.0200.0230.0090.0270.0091.0000.0220.0100.0760.0000.0000.0000.0000.0000.0000.0000.0000.0130.0130.0240.0370.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0090.0090.0000.0000.0000.0000.0110.0000.0070.0000.0000.0030.0000.0120.0000.0100.0000.0150.0140.0000.0000.0000.0820.0000.0000.0000.0000.0000.0280.000
display_90.0120.0000.0670.0680.0000.0120.0340.0120.0140.0000.0170.0000.0221.0000.0000.0450.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0070.0000.0000.0130.0050.0100.0030.0000.0000.0090.0000.0000.0090.0000.0000.0000.0000.0000.0120.0000.0080.0000.0000.0060.0060.0000.0000.0100.0000.0000.0000.0000.0000.0000.000
display_A0.0440.0000.0110.0110.0000.0090.0180.0000.0000.0000.0050.0000.0100.0001.0000.0000.0000.0000.0000.0020.0160.0000.0000.0000.0170.0000.0000.0000.0000.0140.0220.0000.0320.0000.0360.0000.0080.0090.0000.0000.0000.0180.0100.0000.0160.0060.0000.0000.0000.0000.0050.0080.0210.0310.0000.0110.0280.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_A0.0360.0040.4090.4130.0000.0320.1520.0700.0570.0270.0330.0260.0760.0450.0001.0000.0000.0290.0000.0510.0160.0000.0070.0000.0330.0030.0000.0130.0100.0150.0270.0000.0170.0140.0000.0140.0210.0130.0200.0280.0170.0000.0000.0000.0070.0370.0300.0200.0150.0120.0000.0000.0150.0280.0150.0100.0310.0000.0110.0040.0560.0240.0150.0000.0170.0000.0350.000
mailer_C0.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0230.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_D0.0000.0000.1430.1430.0000.0000.0710.0140.0000.0000.0000.0000.0000.0000.0000.0290.0001.0000.0000.0070.0000.0000.0160.0130.0060.0000.0000.0070.0310.0000.0000.0000.0000.0220.0000.0000.0100.0000.0000.0080.0080.0180.0000.0000.0110.0050.0030.0000.0000.0000.0080.0000.0000.0080.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_F0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0210.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.000
mailer_H0.0160.0000.1830.1870.0330.0000.0810.0380.0000.0000.0000.0000.0000.0000.0020.0510.0000.0070.0001.0000.0000.0000.0000.0040.0000.0000.0000.0120.0000.0000.0000.0080.0100.0000.0120.0000.0000.0040.0200.0110.0270.0280.0160.0040.0000.0090.0110.0000.0000.0000.0000.0160.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.000
mailer_J0.0270.0000.0790.0800.0000.0180.0220.0000.0000.0000.0000.0800.0000.0000.0160.0160.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0060.0130.0000.0110.0060.0000.0000.0100.0000.0000.0000.0000.0000.0000.0050.0020.0040.0000.0000.0120.0000.0000.0100.0000.0290.0000.0100.0000.0170.0190.0100.0090.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_L0.0000.0000.0450.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_A0.1140.0260.0480.0520.0140.0220.0000.0150.0130.0190.0200.0000.0000.0230.0000.0070.0000.0160.0000.0000.0000.0001.0000.3530.4630.0100.0900.0430.1220.4290.1120.2310.1920.1650.0680.2740.3460.1420.0400.0920.0410.1020.0000.0830.1370.1000.0470.0150.0690.0620.1180.0460.0140.0700.1170.5810.1820.1400.1180.3120.0080.0000.0000.0000.0000.0000.0020.000
marital_status_B0.1480.0000.0280.0280.0300.0120.0000.0140.0000.0000.0030.0000.0000.0000.0000.0000.0000.0130.0000.0040.0000.0000.3531.0000.1460.0150.0000.2620.2000.2010.1230.2130.1600.0890.0060.0280.0910.1300.0420.0890.1660.0320.0220.0050.0490.0100.0320.0210.0220.1350.0540.0160.0960.0100.0900.2550.1340.0420.0190.1030.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Homeowner0.1670.0280.0700.0750.0240.0040.0090.0040.0010.0130.0370.0000.0130.0000.0170.0330.0000.0060.0000.0000.0000.0000.4630.1461.0000.1740.1410.3560.1170.2560.2450.2850.1790.1000.0310.1240.1760.2120.0760.0050.0630.0680.1220.0120.1290.1160.1170.0080.0310.1440.0900.0830.0350.1520.1120.4250.1960.1150.0350.1620.0110.0000.0000.0000.0000.0000.0080.000
homeowner_Probable Owner0.2030.0080.0230.0210.0000.0180.0080.0000.0000.0000.0160.0000.0130.0000.0000.0030.0000.0000.0000.0000.0000.0000.0100.0150.1741.0000.0090.0340.0290.0170.0000.1180.0410.0000.0000.0460.0350.0170.0360.0690.0130.0290.0000.0500.0310.0360.0370.0130.0000.0000.0170.0000.0030.0410.0360.0360.0000.0060.0000.0440.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Probable Renter0.1400.0000.0000.0000.0000.0150.0020.0000.0000.0000.0060.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.1410.0091.0000.0270.0260.0680.0670.1060.0000.0430.0350.0370.0810.0000.0070.0400.0620.0000.0190.0190.0250.1130.0190.0090.0000.0800.0130.0220.0470.0390.0300.1580.0730.0450.0320.0350.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Renter0.1750.0000.0550.0560.0140.0210.0080.0000.0130.0080.0130.0020.0370.0000.0000.0130.0000.0070.0000.0120.0060.0000.0430.2620.3560.0340.0271.0000.1760.0500.0960.0030.0920.0330.0470.0580.0430.2080.0540.0740.0650.0390.0660.0370.0040.0100.0540.0340.0120.0870.0410.0240.1150.0250.1290.0000.0160.0660.1660.0380.0000.0000.0090.0000.0000.0000.0030.000
hhcomp_1 Adult Kids0.1440.0000.0380.0420.0000.0070.0190.0290.0000.0130.0380.0080.0000.0000.0000.0100.0000.0310.0000.0000.0130.0000.1220.2000.1170.0290.0260.1761.0000.1710.1700.1150.0850.1020.2650.1630.3520.1280.1390.0330.1350.0430.0550.0370.0160.0000.0540.0330.0120.0310.0410.0600.0850.0960.2400.1760.0670.1920.0930.0520.0000.0030.0000.0000.0000.0000.0080.000
hhcomp_2 Adults Kids0.0760.0100.0280.0310.0150.0320.0000.0000.0090.0040.0000.0000.0000.0170.0140.0150.0080.0000.0080.0000.0000.0000.4290.2010.2560.0170.0680.0500.1711.0000.4080.2760.2060.5020.3360.3980.8460.0520.0500.1550.0600.0420.1460.0750.1430.0530.0300.0140.0450.0810.0550.0340.0190.0440.0830.4230.4680.4750.3920.4340.0040.0060.0190.0000.0000.0000.0000.000
hhcomp_2 Adults No Kids0.1060.0260.0540.0530.0000.0060.0080.0110.0000.0000.0130.0000.0200.0000.0220.0270.0000.0000.0000.0000.0110.0000.1120.1230.2450.0000.0670.0960.1700.4081.0000.2740.2040.2630.2170.2280.4830.0110.0710.0640.0410.0220.1240.0750.0370.0000.0730.0410.0040.0070.0530.0390.0040.0210.0600.4190.8720.2720.2000.2170.0000.0050.0270.0000.0000.0000.0140.000
hhcomp_Single Female0.0810.0200.0160.0170.0000.0340.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0060.0000.2310.2130.2850.1180.1060.0030.1150.2760.2741.0000.1380.1780.1470.1540.3270.0450.0570.0360.0370.0000.0550.0240.0380.0890.0380.0660.0240.1670.0290.0780.0570.0640.0470.4940.1590.1840.1350.1470.0140.0090.0000.0000.0000.0000.0000.000
hhcomp_Single Male0.2110.0140.0460.0510.0240.0160.0010.0000.0110.0030.0020.0000.0000.0000.0320.0170.0000.0000.0000.0100.0000.0000.1920.1600.1790.0410.0000.0920.0850.2060.2040.1381.0000.1330.1090.1150.2440.0000.0930.0260.0730.0630.0000.0000.0780.0380.0000.0300.0160.0080.0400.0580.0850.0670.0150.3890.1380.1370.1010.1090.0000.0000.0000.0000.0000.0080.0000.000
kid_category_10.1310.0000.0000.0120.0110.0320.0000.0080.0140.0000.0080.0160.0000.0070.0000.0140.0000.0220.0000.0000.0000.0000.1650.0890.1000.0000.0430.0330.1020.5020.2630.1780.1331.0000.1410.1480.5460.0000.0090.1500.0910.0430.0650.0220.0360.0330.0000.0370.0480.0300.0820.0240.0560.0310.0090.2730.2100.8450.1300.1410.0000.0000.0070.0000.0130.0000.0000.000
kid_category_20.1470.0000.0260.0300.0000.0000.0100.0200.0000.0000.0180.0000.0000.0000.0360.0000.0230.0000.0000.0120.0100.0000.0680.0060.0310.0000.0350.0470.2650.3360.2170.1470.1090.1411.0000.1220.4510.0690.1010.0270.0240.0130.0000.0400.0050.0370.0510.0440.0180.0410.0530.0060.0280.0190.0280.2250.2490.0700.8110.1160.0000.0000.0030.0000.0000.0000.0000.000
kid_category_3+0.1750.0180.0110.0130.0200.0080.0000.0150.0000.0060.0120.0000.0000.0000.0000.0140.0000.0000.0210.0000.0000.0000.2740.0280.1240.0460.0370.0580.1630.3980.2280.1540.1150.1480.1221.0000.4720.0850.0810.0470.0600.0660.0960.0600.1880.0880.0430.0000.0140.0220.0000.0490.0530.0210.0550.2360.2610.1530.0000.9520.0200.0030.0110.0060.0000.0000.0000.006
kid_category_None/Unknown0.1000.0050.0410.0420.0000.0360.0080.0070.0090.0130.0110.0050.0000.0130.0080.0210.0040.0100.0040.0000.0000.0000.3460.0910.1760.0350.0810.0430.3520.8460.4830.3270.2440.5460.4510.4721.0000.0110.1270.1240.1230.0660.1120.0480.1570.0590.0000.0000.0330.0650.0270.0000.0580.0000.0460.5000.4860.5650.4150.4500.0030.0000.0240.0000.0000.0000.0000.000
age_19-240.1350.0000.0380.0380.0000.0000.0000.0000.0000.0000.0290.0000.0000.0050.0090.0130.0000.0000.0000.0040.0000.0000.1420.1300.2120.0170.0000.2080.1280.0520.0110.0450.0000.0000.0690.0850.0111.0000.0950.1360.1850.0520.0590.0450.0550.0080.0450.0270.0110.0240.0390.0000.0450.0480.2700.0180.0220.0240.0720.0300.0140.0000.0000.0000.0000.0000.0000.000
age_25-340.1080.0000.0440.0450.0180.0300.0000.0210.0000.0000.0000.0040.0090.0100.0000.0200.0000.0000.0000.0200.0000.0000.0400.0420.0760.0360.0070.0540.1390.0500.0710.0570.0930.0090.1010.0810.1270.0951.0000.2580.3520.1010.1140.0600.0360.0000.0360.0260.0000.0000.0660.0290.0290.0460.0160.0540.0550.0350.0270.0980.0000.0100.0480.0000.0000.0000.0320.000
age_35-440.1040.0000.0380.0360.0220.0230.0000.0120.0000.0000.0130.0000.0090.0030.0000.0280.0000.0080.0000.0110.0000.0000.0920.0890.0050.0690.0400.0740.0330.1550.0640.0360.0260.1500.0270.0470.1240.1360.2581.0000.5040.1450.1640.0740.0440.0000.0050.0470.0300.0170.0640.0330.0000.0490.0600.0490.0750.1370.0350.0630.0000.0210.0190.0000.0110.0000.0000.000
age_45-540.1380.0130.0190.0230.0070.0000.0000.0000.0090.0020.0050.0140.0000.0000.0000.0170.0000.0080.0000.0270.0000.0000.0410.1660.0630.0130.0620.0650.1350.0600.0410.0370.0730.0910.0240.0600.1230.1850.3520.5041.0000.1980.2230.0000.0370.0250.0270.1020.0250.0190.0080.0550.0250.0430.0760.1060.0050.1210.0060.0390.0000.0240.0000.0000.0100.0000.0220.000
age_55-640.1080.0360.0170.0190.0000.0050.0000.0050.0150.0000.0100.0000.0000.0000.0180.0000.0000.0180.0000.0280.0050.0000.1020.0320.0680.0290.0000.0390.0430.0420.0220.0000.0630.0430.0130.0660.0660.0520.1010.1450.1981.0000.0630.0810.0320.0350.0760.0120.0090.0270.0360.0680.0270.0150.0200.0240.0840.0480.0280.0620.0000.0000.0060.0000.0000.0000.0000.000
age_65+0.1660.0000.0390.0400.0170.0630.0000.0000.0000.0090.0080.0000.0000.0090.0100.0000.0000.0000.0000.0160.0020.0000.0000.0220.1220.0000.0190.0660.0550.1460.1240.0550.0000.0650.0000.0960.1120.0590.1140.1640.2230.0631.0000.0000.0190.0630.0450.0340.0120.0130.0410.0690.0410.0480.0720.0340.1380.0000.0840.0910.0000.0000.0000.0000.0120.0000.0000.000
income_100-124K0.1320.0000.0450.0440.0120.0000.0270.0140.0000.0050.0160.0110.0000.0000.0000.0000.0000.0000.0000.0040.0040.0000.0830.0050.0120.0500.0190.0370.0370.0750.0750.0240.0000.0220.0400.0600.0480.0450.0600.0740.0000.0810.0001.0000.0510.0590.0410.0250.0060.0680.0310.1000.1180.0770.0580.0210.0780.0270.0540.0690.0000.0000.0060.0000.0000.0000.0000.000
income_125-149K0.2040.0000.0000.0000.0350.0270.0050.0000.0210.0000.0000.0000.0110.0000.0160.0070.0000.0110.0000.0000.0000.0000.1370.0490.1290.0310.0250.0040.0160.1430.0370.0380.0780.0360.0050.1880.1570.0550.0360.0440.0370.0320.0190.0511.0000.0720.0510.0310.0110.0830.0380.1220.1440.0940.0710.1350.0350.0280.0220.2030.0000.0000.0000.0130.0000.0000.0000.013
income_15-24K0.1810.0240.0670.0680.0000.0100.0280.0000.0000.0160.0090.0000.0000.0090.0060.0370.0000.0050.0000.0090.0000.0000.1000.0100.1160.0360.1130.0100.0000.0530.0000.0890.0380.0330.0370.0880.0590.0080.0000.0000.0250.0350.0630.0590.0721.0000.0590.0360.0140.0960.0440.1400.1650.1080.0820.0780.0100.0550.0550.0830.0000.0000.0000.0000.0000.0100.0000.000
income_150-174K0.1180.0390.0500.0520.0000.0410.0110.0170.0000.0000.0070.0000.0070.0000.0000.0300.0000.0030.0000.0110.0120.0000.0470.0320.1170.0370.0190.0540.0540.0300.0730.0380.0000.0000.0510.0430.0000.0450.0360.0050.0270.0760.0450.0410.0510.0591.0000.0250.0060.0680.0300.1000.1170.0760.0580.0610.0540.0000.0440.0510.0000.0000.0000.0000.0000.0000.0000.000
income_175-199K0.1490.0000.0440.0430.0000.0260.0240.0000.0010.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0150.0210.0080.0130.0090.0340.0330.0140.0410.0660.0300.0370.0440.0000.0000.0270.0260.0470.1020.0120.0340.0250.0310.0360.0251.0000.0000.0420.0170.0630.0740.0480.0360.0190.0180.0330.0400.0000.0000.0000.0000.0000.0000.0000.0010.000
income_200-249K0.1690.0200.0180.0180.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0690.0220.0310.0000.0000.0120.0120.0450.0040.0240.0160.0480.0180.0140.0330.0110.0000.0300.0250.0090.0120.0060.0110.0140.0060.0001.0000.0170.0000.0280.0330.0200.0130.0390.0000.0450.0160.0170.0000.0000.0000.0000.0000.0000.0000.000
income_25-34K0.1310.0120.0430.0410.0000.0350.0280.0000.0000.0230.0050.0000.0030.0000.0000.0120.0000.0000.0000.0000.0100.0000.0620.1350.1440.0000.0800.0870.0310.0810.0070.1670.0080.0300.0410.0220.0650.0240.0000.0170.0190.0270.0130.0680.0830.0960.0680.0420.0171.0000.0510.1620.1900.1240.0940.0410.0200.0000.0940.0110.0080.0000.0000.0000.0000.0000.0000.000
income_250K+0.1520.0000.0430.0420.0000.0000.0220.0000.0000.0000.0080.0000.0000.0000.0050.0000.0000.0080.0000.0000.0000.0000.1180.0540.0900.0170.0130.0410.0410.0550.0530.0290.0400.0820.0530.0000.0270.0390.0660.0640.0080.0360.0410.0310.0380.0440.0300.0170.0000.0511.0000.0760.0890.0580.0430.0680.0310.0770.0480.0000.0000.0000.0000.0000.0000.0000.0000.000
income_35-49K0.1220.0000.0440.0470.0000.0110.0000.0180.0120.0230.0000.0000.0120.0120.0080.0000.0000.0000.0000.0160.0290.0000.0460.0160.0830.0000.0220.0240.0600.0340.0390.0780.0580.0240.0060.0490.0000.0000.0290.0330.0550.0680.0690.1000.1220.1400.1000.0630.0280.1620.0761.0000.2770.1820.1380.0120.0420.0180.0220.0450.0000.0080.0000.0000.0000.0000.0120.000
income_50-74K0.1790.0000.0240.0260.0000.0120.0150.0000.0000.0000.0000.0020.0000.0000.0210.0150.0110.0000.0000.0030.0000.0000.0140.0960.0350.0030.0470.1150.0850.0190.0040.0570.0850.0560.0280.0530.0580.0450.0290.0000.0250.0270.0410.1180.1440.1650.1170.0740.0330.1900.0890.2771.0000.2140.1620.0830.0290.0640.0390.0370.0000.0090.0000.0000.0130.0000.0120.000
income_75-99K0.1490.0280.0360.0410.0560.0160.0350.0020.0000.0050.0000.0060.0100.0080.0310.0280.0000.0080.0000.0000.0100.0000.0700.0100.1520.0410.0390.0250.0960.0440.0210.0640.0670.0310.0190.0210.0000.0480.0460.0490.0430.0150.0480.0770.0940.1080.0760.0480.0200.1240.0580.1820.2141.0000.1060.0370.0270.0210.0000.0080.0000.0030.0070.0000.0000.0000.0090.000
income_Under 15K0.1380.0000.0480.0490.0000.0000.0240.0090.0040.0070.0160.0150.0000.0000.0000.0150.0000.0170.0000.0000.0000.0000.1170.0900.1120.0360.0300.1290.2400.0830.0600.0470.0150.0090.0280.0550.0460.2700.0160.0600.0760.0200.0720.0580.0710.0820.0580.0360.0130.0940.0430.1380.1620.1061.0000.0000.0170.0190.0490.0350.0000.0050.0030.0000.0000.0000.0110.000
hhsize_10.1190.0040.0390.0400.0000.0420.0000.0000.0000.0130.0000.0000.0150.0000.0110.0100.0000.0000.0000.0000.0170.0000.5810.2550.4250.0360.1580.0000.1760.4230.4190.4940.3890.2730.2250.2360.5000.0180.0540.0490.1060.0240.0340.0210.1350.0780.0610.0190.0390.0410.0680.0120.0830.0370.0001.0000.4800.2820.2070.2250.0170.0000.0000.0000.0000.0000.0000.000
hhsize_20.1280.0200.0570.0540.0000.0060.0000.0000.0090.0000.0120.0000.0140.0060.0280.0310.0000.0000.0000.0000.0190.0000.1820.1340.1960.0000.0730.0160.0670.4680.8720.1590.1380.2100.2490.2610.4860.0220.0550.0750.0050.0840.1380.0780.0350.0100.0540.0180.0000.0200.0310.0420.0290.0270.0170.4801.0000.3120.2290.2490.0000.0080.0180.0000.0000.0000.0060.000
hhsize_30.1520.0000.0220.0270.0090.0240.0000.0000.0130.0000.0000.0170.0000.0060.0000.0000.0000.0000.0000.0000.0100.0000.1400.0420.1150.0060.0450.0660.1920.4750.2720.1840.1370.8450.0700.1530.5650.0240.0350.1370.1210.0480.0000.0270.0280.0550.0000.0330.0450.0000.0770.0180.0640.0210.0190.2820.3121.0000.1350.1460.0000.0000.0080.0000.0120.0000.0000.000
hhsize_40.2230.0000.0500.0490.0000.0120.0080.0000.0030.0000.0210.0000.0000.0000.0310.0110.0250.0000.0000.0000.0090.0000.1180.0190.0350.0000.0320.1660.0930.3920.2000.1350.1010.1300.8110.0000.4150.0720.0270.0350.0060.0280.0840.0540.0220.0550.0440.0400.0160.0940.0480.0220.0390.0000.0490.2070.2290.1351.0000.1070.0000.0060.0000.0000.0000.0000.0000.000
hhsize_5+0.1320.0190.0140.0170.0250.0310.0000.0110.0000.0010.0000.0000.0000.0000.0000.0040.0000.0000.0230.0000.0000.0000.3120.1030.1620.0440.0350.0380.0520.4340.2170.1470.1090.1410.1160.9520.4500.0300.0980.0630.0390.0620.0910.0690.2030.0830.0510.0000.0170.0110.0000.0450.0370.0080.0350.2250.2490.1460.1071.0000.0110.0000.0100.0070.0000.0000.0000.007
campaign_8.00.0170.0000.0630.0640.0000.0140.0000.0110.0000.0000.0180.0270.0820.0100.0000.0560.0000.0000.0000.0000.0000.0000.0080.0000.0110.0000.0000.0000.0000.0040.0000.0140.0000.0000.0000.0200.0030.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0170.0000.0000.0000.0111.0000.0000.0000.0000.0000.0000.4890.000
campaign_13.00.0000.1850.0500.0190.2050.0150.0040.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0060.0050.0090.0000.0000.0000.0030.0000.0000.0100.0210.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0090.0030.0050.0000.0080.0000.0060.0000.0001.0000.0000.0000.0000.0000.5080.000
campaign_18.00.0240.0000.0380.0320.2150.0330.0090.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0190.0270.0000.0000.0070.0030.0110.0240.0000.0480.0190.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0030.0000.0180.0080.0000.0100.0000.0001.0000.0000.0000.0000.6370.000
campaign_25.00.0060.0000.0170.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.0000.0000.0000.500
campaign_26.00.0000.0000.0380.0460.0700.0040.0000.0000.0060.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0110.0100.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0001.0000.0000.2150.000
campaign_30.00.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0470.000
description_TypeA0.0200.0940.0320.0360.1860.0380.0090.0000.0000.0000.0000.0040.0280.0000.0000.0350.0000.0000.0000.0060.0000.0000.0020.0000.0080.0000.0000.0030.0080.0000.0140.0000.0000.0000.0000.0000.0000.0000.0320.0000.0220.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0120.0120.0090.0110.0000.0060.0000.0000.0000.4890.5080.6370.0000.2150.0471.0000.000
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Missing values

2023-05-23T16:56:49.825328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-23T16:56:51.190861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
00True1.880.000000-0.000000-0.000000000000000001010000010000010000010000000010000100000000000
11True10.990.000000-0.000000-0.000000000010000001010000010000010000010000000010000100000000000
22True1.790.000000-0.000000-0.000000000000000001010000010000010000010000000010000100000000000
33False1.790.000000-0.000000-0.000000000000000001010000010000010000010000000010000100000000000
44False1.790.1005590.100559-0.000000000000000001010000010000010000010000000010000100000000000
55False1.790.1620110.162011-0.000000000000000001010000010000010000010000000010000100000000000
66False1.790.1620110.162011-0.000000000000000001010000010000010000010000000010000100000000000
77False1.790.1005590.100559-0.000000000000000001010000010000010000010000000010000100000000000
88False1.790.1005590.100559-0.000000000000000001010000010000010000010000000010000100000000000
99True1.790.000000-0.000000-0.000000000000000001010000010000010000010000000010000100000000000
Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
1519727396True3.190.000000-0.000000-0.000000000000000000010000010000010100000000000001000100000000000
1519827397True1.490.3288590.328859-0.010000000010000000010000010000010100000000000001000100000000000
1519927398True3.190.2163010.216301-0.000100000000000000010000010000010100000000000001000100000000000
1520027399True4.890.000000-0.000000-0.000000000000000000010000010000010100000000000001000100000000000
1520127400True2.790.1039430.103943-0.000000000000000000000000100010000100000000000000010010000000000
1520227401True2.290.000000-0.000000-0.000000000000000000000000100010000100000000000000010010000000000
1520327402True1.500.000000-0.000000-0.000000000000000000000000100010000100000000000000010010000000000
1520427403False3.190.3761760.376176-0.000000000000000000000000100010000100000000000000010010000000000
1520527404True1.490.000000-0.000000-0.000000000000000000000000100010000100000000000000010010000000000
1520627405True1.990.2462310.246231-0.000000000000000000000000100010000100000000000000010010000000000